Tilde, an interdisciplinary data access solution serving GeoNet’s volcano and other hazard data

Abstract Tilde is a new in-house developed solution that the GNS Science’s GeoNet program has recently developed to provide storage and access to low sample rate datasets used to monitor tsunami, landslides, and volcanoes in Aotearoa New Zealand. It includes datasets covering sample rates of 15 s or longer. Time series data are stored and disseminated in JSON and CSV formats, and users can access these through an application programming interface (API) and through graphical user interfaces (GUIs). Tilde’s GUIs were created to allow technical and non-technical users easy access to the available data. The introduction of the Tilde system as one of the GeoNet program delivery channels has represented a big step forward for GeoNet’s volcano data holdings by providing a single point to access all low to medium-sample rate volcano-specific monitoring data. We designed the system and developed a domain model, an API, a graphical data discovery interface, and associated data tutorials. This work leverages the open-by-default data policy for data generated through the GeoNet program. This paper is intended to highlight how we made many of the key decisions that shaped the Tilde system, how they were impacted by our multi-hazard monitoring requirements, and how they have improved access to volcano monitoring data. We conclude with some open questions about the need to develop common standards to share analysis-ready time series data within different disciplines in volcanology and geophysics.

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Publication Details

Journal
Bulletin of Volcanology
Published
2024-11-28
DOI
https://doi.org/10.1007/s00445-024-01786-w
Citations
1
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
1.05

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article

Tilde, an interdisciplinary data access solution serving GeoNet’s volcano and other hazard data

J. B. Hanson, Elisabetta D’Anastasio, Baishan Peng, Joshua Groom et al.
1 citations
Bulletin of Volcanology
Scientific Computing and Data Management
1.05
article

Tilde, an interdisciplinary data access solution serving GeoNet’s volcano and other hazard data

J. B. Hanson, Elisabetta D’Anastasio, Baishan Peng, Joshua Groom, Jeremy Houltham, Callum Morris, Mark Chadwick, Howard Wu, Sue Harvey, Steven Sherburn
article en
1 citations

Abstract

Abstract Tilde is a new in-house developed solution that the GNS Science’s GeoNet program has recently developed to provide storage and access to low sample rate datasets used to monitor tsunami, landslides, and volcanoes in Aotearoa New Zealand. It includes datasets covering sample rates of 15 s or longer. Time series data are stored and disseminated in JSON and CSV formats, and users can access these through an application programming interface (API) and through graphical user interfaces (GUIs). Tilde’s GUIs were created to allow technical and non-technical users easy access to the available data. The introduction of the Tilde system as one of the GeoNet program delivery channels has represented a big step forward for GeoNet’s volcano data holdings by providing a single point to access all low to medium-sample rate volcano-specific monitoring data. We designed the system and developed a domain model, an API, a graphical data discovery interface, and associated data tutorials. This work leverages the open-by-default data policy for data generated through the GeoNet program. This paper is intended to highlight how we made many of the key decisions that shaped the Tilde system, how they were impacted by our multi-hazard monitoring requirements, and how they have improved access to volcano monitoring data. We conclude with some open questions about the need to develop common standards to share analysis-ready time series data within different disciplines in volcanology and geophysics.

Bulletin of VolcanologyVol. 86(12)
GNS Science (NZ)
New Zealand Government, Ministry of Business, Innovation and Employment, National Emergency Management Agency
Openalex Percentile: Top 12%
Scientific Computing and Data Management
1.05
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